Radiation therapy (or radiotherapy) is commonly used in the treatment of various cancers to control the spread of malignant cells. Radiotherapy may be used for curative or adjuvant treatment. When selecting a dose, many other factors are considered by radiation oncologists, including whether the patient is receiving chemotherapy, patient comorbidities, whether radiation therapy is being administered before or after surgery, and the degree of success of surgery. The amount of radiation used in photon radiation therapy varies depending on the type and stage of cancer being treated.
Delivery parameters of a prescribed dose are determined during treatment planning, part of dosimetry. Treatment planning is currently usually performed on dedicated computers using specialized treatment planning software. The planner will try to design a plan that delivers a prescription dose to the tumor and minimizes dose to surrounding healthy tissues. Depending on the radiation delivery method, several angles or sources may be used to sum to the total necessary dose. Proper dosing remains one of the most important considerations of radiation therapy. With proper dosing, ill cells may be targeted and while healthy cells are preserved.
Dosimetric calculations are a crucial component in the accurate delivery and assessment of radiation therapy. As radiation therapy and diagnostics imaging technologies have become more complex, the associated physics calculations have become more resource intensive. These requirements have been largely met by the exponential increase in processor speed and RAM size, but sometimes outstrip the pace of computer technology even for conventional, deterministic calculation techniques. For example, TomoTherapy, Inc.'s TomoHD ships with a 14 node calculation cluster. Non-deterministic algorithms, such as the Monte Carlo method, demand even greater computing resources than conventional algorithms, but generally offer superior dose calculation accuracy. This is particularly true for complex, heterogeneous treatment scenarios such as particle therapy treatment planning. The use of Monte Carlo simulations is highly desirable for dose calculation in radiation therapy as part of treatment planning or verification. In the recent years, Monte Carlo simulations brought the much-needed dosing accuracy to cancer patients. Monte Carlo simulations represent the gold standard in radiation dose calculation since they include the real physics of the interactions of photons with materials.
Notwithstanding its great results, Monte Carlo simulations have not yet been put into routine clinical use due to long calculation times. For example, it has been reported that times of more than 100 CPU hours are required to simulate proton beam treatment plans when using approximately 2×107 primary protons per field. Thus, while Monte Carlo techniques are widely seen as the gold standard of radiation dose calculations, they are only sparingly used clinically in favor of faster, less resource intensive algorithms at the cost of dosimetric accuracy. The primary barrier to widespread adoption of Monte Carlo techniques has been the requirement of large computing resources to achieve clinically relevant run times, particularly in particle therapy applications. These resources, usually in the form of a computing cluster, require a sizable infrastructure investment as well as associated utility, maintenance, upgrades, and personnel costs. These costs make full, analog Monte Carlo methods effectively unfeasible for routine clinical use. This is especially true in particle therapy.
Large-scale, full Monte Carlo simulations and other resource intensive algorithms are often simply considered unfeasible for clinical settings. Very few, if any, clinics are able to use Monte Carlo simulations for routine verifications of their proton therapy plans. Each simulation may take as many as 240 CPU hours for tens of millions of primaries. The majority of the clinical dosimetry systems are based on faster, less resource intensive algorithms, at the cost of dosimetry accuracy.
Cloud computing is a name given to a set of technologies offered as services over the Internet. Pricing is usually based on the pay-as-you-go model, generally billed in hourly increments, and without set contract periods. This scheme allows cloud services to offer on-demand computing infrastructure, sized to fit the user's monetary needs. Cloud computing has become feasible because of the economies of scale afforded by the commoditization of computer hardware, extensive availability of high bandwidth networks, and growth of free, open source software, including operating systems, such as Linux, and virtual machine software.
For clinical usage, cloud computing has many advantages. Cloud resources can be scaled to meet patient and physics quality assurance demand as it fluctuates on a daily basis. Typical computing clusters often face bursts of usage where they are under-utilized most of the day and night and over-queued at peak periods. The cloud paradigm is particularly well suited for one-off calculations, such as machine commissioning and vault shielding calculations, for which a very large cluster might be desirable, but expanding to even a small cluster would be prohibitively expensive for a single run. Also, hardware upgrade and maintenance is taken care of by the provider, rather than by the user.
It would thus be desirable to find a way to improve dosing accuracy, make Monte Carlo calculations more feasible and accessible to clinics and hospitals, and to reduce the costs and computation times associated therewith. Monte Carlo calculations would be particularly well suited to cloud style distributed computing. This is so because the primary particle histories are completely independent of one another, requiring no communication between processes. Monte Carlo calculations, while parallel, need not maintain data or timing synchronization during execution. Until now, however, Monte Carlo calculations in a cloud environment have not been applied to medical physics calculations. Grid computing has been implemented in medical physics calculations, but it is plagued by the same disadvantages as cluster computing. It is inefficient, expensive, and less accessible for medical facilities, despite being widely considered a forerunner to cloud computing.
There is a need to perform medical physics calculations using virtual computer resources versus local dedicated hardware. The present invention satisfies this demand.